There may be provided a method for electrical scooter alert, the method may include sensing sensed information about an environment of a vehicle; detecting, based on the sensed information, a situation related to the environment; detecting, based on the sensed information, a electrical scooter within the environment; predicting, using a machine leaning process and based in the situation, a future behavior of the electrical scooter and an impact of the future behavior of the electrical scooter on a future progress of the vehicle; and responding to the predicting.
Legal claims defining the scope of protection, as filed with the USPTO.
2. The method according to claim 1 wherein the responding to the predicting is responsive to a probability distribution of potential future behaviors of the electrical scooter.
3. The method according to claim 1 wherein the responding is responsive to an existence of at least one potential relevant future behavior of the electrical scooter, wherein each of the at least potential relevant future behavior of the electrical scooter (a) has probability of occurrence that exceeds a threshold, and (b) impacts the future progress of the vehicle when occurs.
4. The method according to claim 1 wherein the responding is selected out of generating a human perceivable information about the predicting, requesting an autonomous module to drive the vehicle based on the predicting; proposing a route of the vehicle; storing an outcome of the predicting; activating a driving mode in response to the outcome of the predicting; activating safety measures in response to the outcome of the predicting; and generating a human perceivable alert outside the vehicle.
5. The method according to claim 1 comprising classifying the electrical scooters to classes; and wherein the training process comprises generating situation behavioral information indicative of behaviors of electrical scooters of different classes at different situations.
6. The method according to claim 1 wherein the generating of the electrical scooter identifiers comprises generating signatures of the electrical scooters.
7. The method according to claim 6 comprising clustering the signatures of electrical scooters to generate electrical scooter classes.
9. The non-transitory computer readable medium according to claim 8 wherein the responding to the predicting is responsive to a probability distribution of potential future behaviors of the electrical scooter.
10. The non-transitory computer readable medium according to claim 8 wherein the responding is responsive to an existence of at least one potential relevant future behavior of the electrical scooter, wherein each of the at least potential relevant future behavior of the electrical scooter (a) has probability of occurrence that exceeds a threshold, and (b) impacts the future progress of the vehicle when occurs.
11. The non-transitory computer readable medium according to claim 8 wherein the responding is selected out of generating a human perceivable information about the predicting, requesting an autonomous module to drive the vehicle based on the predicting; proposing a route of the vehicle; storing an outcome of the predicting; activating a driving mode in response to the outcome of the predicting; activating safety measures in response to the outcome of the predicting; and generating a human perceivable alert outside the vehicle.
12. The non-transitory computer readable medium according to claim 8 that stores instructions for classifying the electrical scooters to classes; and wherein the training process comprises generating situation behavioral information indicative of behaviors of electrical scooters of different classes at different situations.
13. The non-transitory computer readable medium according to claim 8 wherein the generating of the electrical scooter identifiers comprises generating signatures of the electrical scooters.
14. The non-transitory computer readable medium according to claim 13 that stores instructions for clustering the signatures of electrical scooters to generate electrical scooter classes.
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July 23, 2021
November 12, 2024
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